02.01.2020
©Joachim Wendler - stock-adobe.com
Nine Papers in 2020 Highlight Scientific Impact
We are happy to announce that MCML researchers are represented in 2020 with nine papers in highly-ranked journals. Congrats to our researchers!
Conditional out-of-distribution generation for unpaired data using transfer VAE.
Bioinformatics 36.Supplement 2. Dec. 2020. DOI
Sampling uncertainty versus method uncertainty: a general framework with applications to omics biomarker selection.
Biometrical Journal 62.3. May. 2020. DOI
Large-scale benchmark study of survival prediction methods using multi-omics data.
Briefings in Bioinformatics. Aug. 2020. DOI
In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning.
Journal of Extracellular Vesicles 9.1. Jul. 2020. DOI
Predicting antigen specificity of single T cells based on TCR CDR3 regions.
Molecular Systems Biology 16.8. Aug. 2020. DOI
Generalizing RNA velocity to transient cell states through dynamical modeling.
Nature Biotechnology 38. Aug. 2020. DOI
Targeted pharmacological therapy restores β-cell function for diabetes remission.
Nature Metabolism 2. Feb. 2020. DOI
Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning.
Nucleic Acids Research 48.20. Nov. 2020. DOI
Predicting personality from patterns of behavior collected with smartphones.
Proceedings of the National Academy of Sciences 117.30. Jul. 2020. DOI
Related
15.01.2026
Blind Matching – Aligning Images and Text Without Training or Labels
CVPR 2025 research from Daniel Cremers’ group shows how images and text can be aligned without training data, labels, or paired examples.
08.01.2026
High-Res Images, Less Wait: A Simple Flow for Image Generation
ECCV 2024 research led by Björn Ommer’s team enables faster high-resolution image generation by boosting diffusion models with flow matching.
©Joachim Wendler - stock-adobe.com
02.01.2026
MCML Researchers in Highly-Ranked Journals
We are excited to announce that MCML researchers have four papers published in highly-ranked journals in 2026.
18.12.2025
"See, Don’t Assume": Revealing and Reducing Gender Bias in AI
ICLR 2025 research led by Zeynep Akata’s team reveals and reduces gender bias in popular vision-language AI models.